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Sampling Big Ideas in Sublinear Algorithms

Offered By: Simons Institute via YouTube

Tags

Algorithm Design Courses Data Analysis Courses Data Summarization Courses Scalability Courses Streaming Data Courses Sublinear Algorithms Courses

Course Description

Overview

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Explore the power of random sampling in enhancing data analysis scalability through this comprehensive lecture by Edith Cohen from Tel Aviv University and Google. Delve into the design and applications of weighted and coordinated sampling schemes, with a focus on algorithmic simplicity and practicality in streamed or distributed data contexts. Learn how samples serve as versatile summaries that can be directly applied or integrated into data analysis processes. Discover key concepts and big ideas in sublinear algorithms, emphasizing their role in improving the efficiency of complex data analysis tasks. Gain insights into the latest developments in this field as part of the Sublinear Algorithms Boot Camp at the Simons Institute.

Syllabus

Sampling Big Ideas in Sublinear Algorithms


Taught by

Simons Institute

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